Microsoft Technology Licensing, LLC

Datachat.ai

Wisconsin Alumni Research Foundation

Twitter, Inc.

Pivotal Software, Inc.

Samsung Electronics Co., Ltd.

Jignesh Patel

Nikhil Teletia

Kwanghyun Park

Jae Young Do

Rogers Jeffrey Leo John

Alan D Halverson

Yinan Li

Sanjeev Raghavendra Kulkarni

Navneet Sankara Potti

Siddharth Taneja

Maosong Fu

Karthik Ramasamy

Vinitha Reddy Gankidi

Vikas Kedigehalli

Robert Konrad Claus

Christopher Kellogg

Sailesh Mittal

Ushmal Ramesh

Jiatong Li

Nikunj Bhagat

Avrilia Floratou

Sushma Devendrappa

Yiwen Zhu

Taeil Um

Anja Gruenheid

Ling Zhang

Navneet Sankara Potty

Harshad Deshmukh

Adalbert Gerald Soosai Raj

Amos Kendall

Junda Chen

Dylan Paul Bacon

Shaleen Deep

Regular expression ('regex') decomposition and evaluation is disclosed. In an aspect of the disclosure, a literal component and a pattern component in a regex are determined. A plurality of evaluation processes is determined based on the regex, including a first evaluation process configured to identify text that matches the literal component and the pattern component. The evaluation processes are executed with respect to a sample of data to determine performance characteristics including a performance characteristic respective to each evaluation process. An evaluation process of the evaluation processes is selected based on the determined performance characteristics. The selected evaluation process is executed with respect to further data to retrieve results that satisfy the selected evaluation process. In another aspect, the pattern component is decomposed into subcomponents. In another aspect, the evaluation processes include an evaluation process that specifies a process to provide the regex to a regex engine for execution thereof.

Regular expression decomposition and evaluation

A device configured to perform data analytics comprising a memory and a processor may be configured to perform the techniques described in this disclosure. The memory may store multi-dimensional data. The processor may receive a sequence of inputs defining a recipe for analyzing the multi-dimensional data according to a language sub-surface specifying a natural language containment hierarchy defining a grammar for a natural language as a hierarchical arrangement of a plurality of language sub-surfaces. The processor may also receive data indicative of a summarized narration of the recipe and parameterize a field of the summarized narration to insert a user adjustable parameter that enables manipulation of the underlying recipe and obtain a parameterized summary that includes the user adjustable parameter. The processor may next present, via a first user interface, the parameterized summary.

Parameterized narrations for data analytics systems

In general, techniques are described for various aspects of accessing datasets. A device comprising a memory configured to store the dataset, and a processor may be configured to perform the techniques. The processor may expose a language sub-surface specifying a natural language containment hierarchy defining a grammar for a natural language as a hierarchical arrangement of a plurality of language sub-surfaces. The processor may receive a query to access the dataset, the query conforming to a portion of the natural language provided by the exposed language sub-surface. The processor may transform the query into one or more statements that conform to a formal syntax associated with the dataset, access, based on the one or more statements, the dataset to obtain a query result, and output the query result.

Conversational syntax using constrained natural language processing for accessing datasets

A device comprising a processor and a memory may be configured to perform various aspects of the techniques described in this disclosure. The processor may present, via a first portion of a user interface, an interactive text box in which a user may enter the data indicative of the current input, present, via a second portion of the user interface, an interactive log of previous inputs entered prior the current input, and present, via a third portion of the user interface, a graphical representation of result data obtained responsive to the data indicative of the current input. The second portion of the user interface and the third portion of the user interface may be separately scrollable but coupled such that interactions in either the second portion of the user interface or the third portion of the user interface synchronize the second portion of the user interface and the third portion of the user interface. The memory may store the data indicative of the current input.

User interface for data analytics systems

In general, techniques are described by which to provide constrained natural language processing. A device comprising a processor and a memory may be configured to perform the techniques. The processor may expose a language sub-surface specifying a natural language containment hierarchy defining a grammar for a natural language as a hierarchical arrangement of language sub-surfaces. The processor may also receive the data indicative of the input that conforms with the portion of the language provided by the exposed language sub-surface, and process the data indicative of the input to identify an intent of the user from a plurality of intents associated with the portion of the language. The processor may next execute a process associated with the identified intent to perform an operation. The memory may store the data indicative of the input.

Constrained natural language processing

A split-index can be employed for access to external data. The index can be created on a primary data storage system for data stored externally on a secondary data storage system. After creation, the index can be utilized to expedite at least query execution over the externally stored data. The index can be updated upon detection of changes to data. Further, even when the index is not completely up to date, the index can be exploited for query execution. Furthermore, hybrid execution is enabled with the index and without the index.

External data access with split index

Methods, systems, and computer program products for accelerate database through runtime code generation are described. A system provides a library of transformation functions that each transform an original function in an intermediate representation into a transformed function in the intermediate representation. The system receives a query. The system obtains an original database function used to execute the query, the original database function being in the intermediate representation. The system generates a new function that is a query specialized version of the original database function. The system generates a result for the query, including executing the new function instead of the original database function.

Database acceleration through runtime code generation

Replicating a primary application cache that serves a primary application on one network node into a secondary application cache that serves a secondary application on a second network node. Cache portions that are within the primary application cache are identified, and then identifiers (but not the cache portions) are transferred to the second network node. Once these identifiers are received, the cache portions that they identify may then be retrieved into the secondary application caches. This process may be repeatedly performed such that the secondary application cache moves towards the same state as the primary application cache though the state of the primary application cache also changes as the primary application operates by receiving read and write requests.

Application cache replication to secondary application(s)

A system and method for data stream processing. Two or more instances are connected as a topology, wherein at least one of the instances is a spout and at least one of the instances is a bolt. The topology is submitted to a scheduler, wherein the service scheduler receives resource offers from a cluster manager representing computing resources available on one or more of cluster nodes and determines resources to accept and computations to run on the accepted computing resources. The topology is scheduled as one or more jobs, wherein each job includes two or more containers, including a first container and a second container, the first container including a topology master and the second container including a stream manager and one or more stream processing system (SPS) instances, wherein each SPS instance represents one of the instances in the topology.

Stream processing at scale

This specification describes methods, systems, and computer program products for maintaining data representing where each data block of multiple data blocks are stored among multiple computing nodes. Each computing node generates a respective locality summary based on locally stored data blocks, and submits the locality summary to a controlling computing node.

Determining data locality in a distributed system using aggregation of locality summaries

Replicating a primary application cache within a secondary application cache

The automated recovery of the warmth of cache of an application that has been subject to a running state change that degraded the warmth of the cache. To prepare for a loss in warmth, the cache portion identifiers are captured, and corresponding cache portions are stored in an external store. Thereafter, the application experiences changes in state. In order to warm the application cache more quickly, cache portions identified by the captured cache portion identifiers are retrieved from the external data store and placed in the application cache.

Automatic recovery of application cache warmth

Domain specific programming is facilitated through the use of a conversational interface using natural language commands from the user and natural language cues to the user. The natural language conversation provides the actual program and thus can be saved and edited for future use.

Conversational programming interface

A database system converts a multi-table relational database into a wide table incorporating all of the information of the relational database tables and converts queries for the relational database system into a form applicable to the wide table. Dictionary compression and/or columnar store allow faster query processing despite a substantially larger size of the wide table.

Database system with highly denormalized database structure

Growing community of inventors

Madison, WI, United States of America

Jignesh Patel